| 引用本文: | 官涤,高大文,任南琪.巢湖叶绿素a与环境因子耦合效应的多元分析[J].哈尔滨工业大学学报,2011,43(12):68.DOI: |
| GUAN Di,GAO Da-wen,REN Nan-qi.Multivariate analysis between chlorophyll-a and coupling of environmental factors in Chaohu Lake[J].Journal of Harbin Institute of Technology,2011,43(12):68.DOI: |
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| 摘要: |
| 针对巢湖西半湖富营养化情况,利用实测资料,通过相关分析和多元逐步回归统计,分析环境多元耦合效应与水体叶绿素a质量浓度之间的关系,找出显著环境因子,建立多元逐步回归方程,预测叶绿素a质量浓度变化．结果表明,巢湖西半湖叶绿素a质量浓度夏季高冬季低,其变化受多个环境因子共同影响,相关性较显著的为气温、水位、CODMn等．在各环境因子间的多元耦合效应下,水文气象因子对水华的驱动作用较强．研究结果可为巢湖水华控制预报工作提供科学依据． |
| 关键词: 巢湖 水华 叶绿素a 多元耦合 多元逐步回归 |
| DOI： |
| 分类号:X524 |
| 文献标识码:A |
| 基金项目:国家创新研究群体基金项目(50821002);城市水资源与水环境国家重点实验室开放研究项目(ESK201021);黑龙江省科学技术计划项目(GB08C204-01);中央高校基本科研业务费资助项目(HEUCF100213);黑龙江省教育厅科学技术研究项目(11553049)． |
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| Multivariate analysis between chlorophyll-a and coupling of environmental factors in Chaohu Lake |
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GUAN Di1,2,GAO Da-wen1,REN Nan-qi1
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(1.State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 150090 Harbin, China, di.guan@yahoo.com.cn; 2. College of Aerospace and Civil Engineering, Harbin Engineering University, 150001 Harbin, China)
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| Abstract: |
| Based on the eutrophication appeared in Chaohu Lake, we analyzed the relation between the multifactor coupling and chlorophyll-a (Chla) with monitor data from 2005 to 2009 in western lake, and by correlation analysis and stepwise multiple regression, we inferred the dominant factors of algal blooms and established the prediction model of Chla variation. The results indicated that seasonal trend of Chla concentration was greater in summer and lower in winter in western lake. The Chla biomass was influenced by multiple effects of environmental factors, in which the dominant ones were air temperature, water level and CODMn. Under multifactor coupling environment, meteorologic and hydrologic factors were most significant to algal blooms. These results might provide scientific foundations for prediction and control of harmful algal blooms in Chaohu Lake. |
| Key words: Chaohu Lake harmful algal blooms Chlorophyll-a multifactor coupling stepwise multiple regression |
